How to load data from Pendo to Snowflake destination
Learn how to use Airbyte to synchronize your Pendo data into Snowflake destination within minutes.
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say
Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
How to Sync to Manually
Step 1: Understand Pendo API Documentation
Begin by familiarizing yourself with Pendo's API documentation. This will provide you with the necessary endpoints, authentication methods, and data structures available for extracting the required data. Note the API rate limits and any pagination requirements for large datasets.
Step 2: Set Up API Authentication
To access Pendo's API, you'll need to authenticate using API keys or tokens. Navigate to the Pendo application, generate your API key, and document it securely. This key will be used for all subsequent API requests to ensure authorized access.
Step 3: Extract Data from Pendo
Write a script (using Python, Node.js, or another language of your choice) to make HTTP GET requests to the relevant Pendo API endpoints. Ensure the script handles pagination if the dataset is large. Parse the JSON responses to extract the data you need, and store it in a temporary format, such as CSV or JSON files, on your local machine or a cloud storage service.
Step 4: Prepare Data for Snowflake
Format the extracted data into a structure that matches your Snowflake table schema. If necessary, transform the data by cleaning, aggregating, or normalizing it. Use tools like Pandas in Python to handle any complex data transformations or to convert the data into CSV format, which is optimal for loading into Snowflake.
Step 5: Set Up Snowflake Environment
Log into your Snowflake account and ensure you have a database and schema ready to receive the data. Create a table if it does not already exist, ensuring that the table schema matches the structure of your prepared data. Define the necessary permissions and roles to allow data loading.
Step 6: Load Data into Snowflake
Utilize Snowflake's `COPY INTO` command to load the prepared CSV files into your Snowflake table. First, upload the CSV files to a Snowflake-compatible stage, such as an internal stage or an external stage (like Amazon S3 or Azure Blob Storage). Then execute the `COPY INTO` command to import the data from the stage into your Snowflake table. Monitor the process and check for any errors or data discrepancies.
Step 7: Validate and Verify Data Integrity
After the data is loaded into Snowflake, perform a thorough validation to ensure data integrity. Run SQL queries to verify row counts, check for duplicates, and confirm that all fields have been populated correctly. Compare the results against the original data extracted from Pendo to ensure accuracy. Document any issues and resolve them as necessary, possibly by re-extracting and re-loading data.
By following these steps, you can successfully transfer data from Pendo to Snowflake without relying on third-party connectors or integrations.